Functions reference list

For reference, here is a list of some useful functions.

If you have trouble with any of these functions, try reading the documentation with ?function_name

Remember: all these functions take the data first.

• filter()

  • Subset rows using column values

• mutate()

  • Create and modify delete columns

• rename()

  • Rename columns

• select()

  • Subset columns using their names and types

• summarise(); summarize()

  • Summarise each group to fewer rows

• group_by(); ungroup()

  • Group by one or more variables

• arrange()

  • Arrange rows by column values

• count(); tally()

  • Count observations by group

• distinct()

  • Subset distinct/unique rows

• pull()

  • Extract a single column

• ifelse()

  • useful for coding of binary variables

• case_when()

  • useful for recoding (when ifelse is not enough)

• separate()

  • separate two variables by some separator

• pivot_wider()

  • turn data into wide format

• pivot_longer()

  • turn data into long format

Task 1

Load the tidyverse and janitor packages.

If tidyverse or janitor packages are not installed yet (it will say [package] not found). Your first task, is then to install them. Note: this might take you a little bit of time.

Task 2

Read in the already cleaned palmerpenguins data set using

• read_csv
• the following URL: https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/inst/extdata/penguins.csv

Assign the resulting data to penguins.

Then take a look a look at it using glimpse.

What kind of variables can you recognize?

Response:

Task 3

Only keep the variables: species, island and sex.

Only keep variables 2 to 4.

Remove the column year.

Only include columns that contain “mm” in the variable name.

Task 4

Rename island to location.

Task 5

Filter the data so that species only includes Chinstrap.

Filter the data so that species only includes Chinstrap or Gentoo.

Filter the data so it includes only penguins that are male and of the species Adelie.

Task 6

Create three new variables that calculates bill_length_mm and bill_depth_mm and flipper_length_mm from millimeter to centimeter.

Tip: divide the length value by 10.

Create a new variable called bill_depth_cat which has two values:

• Everything above a bill depth of 18mm and 18mm itself is “high”
• Everything below a bill depth of 18mm is “low”

Create a new variable called species_short.

• Adelie should become A
• Chinstrap should become C
• Gentoo should become G

Task 7

Calculate the average body_mass_g per island.

If you haven’t done so already, try using the %>% operator to do this.

Task 8

Use the pipe operator (%>%) to do all the operations below.

1. Filter the penguins data so that it only includes Chinstrap or Adelie.
2. Rename sex to observed_sex
3. Only keep the variables species, observed_sex, bill_length_mm and bill_depth_mm
4. Calculate the ratio between bill_length_mm and bill_depth_mm
5. Sort the data by the highest ratio

Try to create the pipe step by step and execute code as you go to see if it works.

Once you are done, assign the data to new_penguins.

Calculate the average ratio by species and sex, again using pipes.

Task 9

Count the number of penguins by island and species.

Task 10

Below is a dataset that needs some cleaning.

Use the skills that you have learned so far to turn the data into a tidy dataset.

animal_friends <- data.frame(
  Names = c("Francis", "Catniss", "Theodor", "Eugenia"),
  TheAnimals = c("Dog", "Cat", "Hamster", "Rabbit"),
  Sex = c("m", "f", "m", "f"),
  a_opterr = c("me", "me", "me", "me"),
  `Age/Adopted/Condition` = c("8/2020/Very Good", "13/2019/Wild", "1/2021/Fair", "2/2020/Good")    
) 

Start here:

If you are done, turn the final data into long format.